Category: deep learning

  • MLflow SHAP & Transformers

    MLflow SHAP & Transformers

    The post covers simplified MLflow projects for reproducible and reusable data science code. It details local environment setup, ElasticNet model optimization, and SHAP explanations for breast cancer, diabetes, and iris datasets. Additionally, it showcases MLflow Sentence Transformers for a chatbot and translation. This demonstrates their powerful interface for managing transformer models from libraries like Hugging…

  • Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM

    Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM

    The data science project involves evaluating various sales forecasting algorithms in Python using a Kaggle time-series dataset. The forecasting algorithms include tslearn, Random Walk, Holt-Winters, SARIMA, GARCH, Prophet, LSTM and Di Pietro’s Model. The goal is to predict next month’s sales for a list of shops and products, which slightly changes every month. The best…

  • Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM

    Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM

    We predict the Remaining Useful Life (RUL) of NASA turbofan jet engines by comparing the statsmodels OLS, ML SciKit-Learn regression vs LSTM Keras in Python. The input dataset is the Kaggle version of the public dataset for asset degradation modeling from NASA. It includes Run-to-Failure simulated data from turbo fan jet engines.

  • Hugging Face NLP, Streamlit, PyGWalker, TF & Gradio App

    Hugging Face NLP, Streamlit, PyGWalker, TF & Gradio App

    The objective of this project is to democratize and advance ML/AI for everyone by building the large open-source showcase of Hugging Face NLP, Streamlit/Dash Jupyter PyGWalker Exploratory Data Analysis (EDA), TensorFlow Keras and Gradio App deployment. There is no better way of showcasing your results than creating an interactive demo to try them out! This…

  • Dividend-NG-BTC Diversify Big Tech

    Dividend-NG-BTC Diversify Big Tech

    SEO Title: Can Dividends, Natural Gas and Crypto Diversify Big Techs? The objective of this project is to implement several basic Quant Trading (QT) optimization ideas by analyzing tech growth, dividend stocks and highly volatile assets (commodities and crypto) in terms of risk/return optimization scenarios and (potential) diversification options. We will use the financial APIs…

  • Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks

    Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks

    This study aims to implement and evaluate the K-means algorithm for ranking/clustering S&P 500 stocks based on average annualized return and volatility. The second goal is to detect anomalies in the best performing S&P 500 stocks using the Isolation Forest algorithm. Additionally, anomalies in the S&P 500 historical stock price time series data will be…

  • NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading

    NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading

    The study presents a machine learning-focused analytical approach to optimize NVIDIA’s stock performance using moving average crossovers and aims at comparing the outcomes with simple RNN mean reversal trading strategies. The steps taken involve preparing the stock data, calculating moving averages and drawdowns, plotting heatmaps of returns and drawdowns, and predicting returns and cumulative returns…

  • Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    In the green energy sector, being able to forecast the electricity usage is a core part of any electricity retailer’s business.  The business problem is as follows: Given the historical power consumption, what is the expected power consumption for the next 365 days? In this study, we will address the problem by building a reliable…

  • Image Based Fast Forest Fire Detection with TensorFlow

    Image Based Fast Forest Fire Detection with TensorFlow

    A recent study showcases the use of artificial intelligence (AI) and deep learning (DL) for efficient wildfire prediction and management. Utilizing a fast DL approach based on the TensorFlow Convolution Neural Network (CNN) algorithm, researchers trained models to distinguish between fire and non-fire images using a public-domain dataset. The implemented system predicted fires accurately and…

  • Robust Fake News Detection: NLP Algorithms for Deep Learning and Supervised ML in Python

    Robust Fake News Detection: NLP Algorithms for Deep Learning and Supervised ML in Python

    The project aims at setting up a robust system for fake news detection using Python. The system adopts a hybrid framework, leveraging Natural Language Processing (NLP) techniques to classify text-based fake vs real news. Involving exploratory data analysis, multi-model training, testing, validation, and performance metrics comparison, it assesses different Deep Learning, Supervised Machine Learning, and…

  • An Overview of Video Games in 2023: Trends, Technology, and Market Research

    An Overview of Video Games in 2023: Trends, Technology, and Market Research

    The gaming industry is rapidly growing, projected to reach a revenue of $365.6 billion in 2023. Major trends include Web3 gaming, AI integration, and a push for consolidation. Fashion brands collaborate for virtual sales, and advances in gaming technology, such as AR/VR and cloud-based gaming, promise an even more immersive experience for gamers.

  • Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%

    Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%

    In 2023, the global card industry is projected to suffer $36.13 billion in fraud losses. This has necessitated a priority focus on enhancing credit card fraud detection by banks and financial organizations. AI-based techniques are making fraud detection easier and more accurate, with models able to recognize unusual transactions and fraud. The post discusses a…

  • Top Fast-Growing Apps in 2023

    Top Fast-Growing Apps in 2023

    The OKTA Business at Work report and blogs by Leon Zucchini discuss the fastest-growing and new app categories. Key trends include the growth of collaboration, communication, and travel apps, and the adoption of multi-cloud. Ten notable growing apps are Kandji, Grammarly, Bob, Notion, Prisma Access, Navan, GitLab, Ironclad, Terraform Cloud, and Figma. Emerging apps include…

  • Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons

    Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons

    Globally, cardiovascular disease (CVD) is the primary cause of morbidity and mortality, accounting for more than 70% of all fatalities. Machine learning (ML) can be used to predict the risk of a heart attack. The algorithms used for this task would be supervised ML algorithms, such as Random Forest, Logistic Regression, Support Vector Machines, etc.…

  • Risk-Aware Strategies for DCA Investors

    Risk-Aware Strategies for DCA Investors

    Dollar-Cost Averaging (DCA) is an investment approach that involves investing a fixed amount regularly, regardless of market price. It offers benefits such as risk reduction and market downturn resilience. It’s useful for beginners and can be combined with other strategies for a disciplined investment approach. References include Investopedia and Yahoo Finance.

  • Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)

    Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)

    The project utilizes AI-driven cardiovascular medicine with a focus on early diagnosis of heart disease using Artificial Neural Networks (ANN). Aiming to improve early detection of heart issues, the project processed a dataset of 303 patients using Python libraries and conducted extensive exploratory data analysis. A Sequential ANN model was subsequently built, revealing excellent performance…

  • Overview of AWS Tech Portfolio 2023

    Overview of AWS Tech Portfolio 2023

    This summary focuses on the extensive capabilities of Amazon Web Services (AWS) by 2023, highlighting its 27% year-on-year growth and a net sales increase to $127.1 billion. AWS emerges as the top cloud service provider, offering over 200 services including compute, storage, databases, networking, AI, and machine learning. It is constantly expanding operations, having opened…

  • Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23

    Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23

    This study applies the Deep Reinforcement Learning (DRL) algorithm to USA stocks with +4% DIV in 2022-23, focusing on Altria Group, Inc. The study addresses accurate stock price predictions and the challenges in traditional methods. Recent advances in DRL have shown improved accuracy in stock forecasting, making it suitable for turbulent markets and investment decision-making.

  • JPM Breakouts: Auto ARIMA, FFT, LSTM & Stock Indicators

    JPM Breakouts: Auto ARIMA, FFT, LSTM & Stock Indicators

    The post discusses predicting JPM stock prices for 2022-2023 using several predictive models like ARIMA, FFT, LSTM, and Technical Trading Indicators (TTIs) such as EMA, RSI, OBV, and MCAD. The ARIMA model used historical data, while the partial spectral decompositions of stock prices served as features for the FFT model. TTIs were calculated to validate…

  • LSTM Price Predictions of 4 Tech Stocks

    LSTM Price Predictions of 4 Tech Stocks

    The given content explains the process of using Exploratory Data Analysis (EDA) and Long Short-Term Memory (LSTM) Sequential model for comparing the risk/return of four major tech stocks: Apple, Google, Microsoft, and Amazon, considering the tech scenario in 2023. The analysis involves examining stock price patterns, their correlations, risk-return assessment, and predicting stock prices using…